Sufficient and necessary conditions of stochastic permanence and extinction for stochastic logistic populations under regime switching

In this paper, we prove that a stochastic logistic population under regime switching controlled by a Markov chain is either stochastically permanent or extinctive, and we obtain the sufficient and necessary conditions for stochastic permanence and extinction under some assumptions. In the case of stochastic permanence we estimate the limit of the average in time of the sample path of the solution by two constants related to the stationary probability distribution of the Markov chain and the parameters of the subsystems of the population model. Finally, we illustrate our conclusions through two examples

A Text Mining and Ensemble Learning Based Approach for Credit Risk Prediction

Traditional credit risk prediction models mainly rely on financial data. However, technological innovation is the main driving force for the development of enterprises in strategic emerging industries, which is closely related to enterprise credit risk. In this paper, a novel prediction framework utilizing technological innovation text mining data and ensemble learning is proposed. The empirical data from China listed enterprises in strategic emerging industries were applied to construct prediction models using the classification and regression tree model, the random forest model and extreme gradient boosting model. The results show that the model uses the technological innovation text mining data proven to have significant predict ability, and top management teamꞌs attention to innovation variables offer the best prediction capacities. This work improves the application value of enterprise credit risk prediction models in strategic emerging industries by embedding the mining of technological innovation text information

A Hybrid Technological Innovation Text Mining, Ensemble Learning and Risk Scorecard Approach for Enterprise Credit Risk Assessment

Enterprise credit risk assessment models typically use financial-based information as a predictor variable, relying on backward-looking historical information rather than forward-looking information for risk assessment. We propose a novel hybrid assessment of credit risk that uses technological innovation information as a predictor variable. Text mining techniques are used to extract this information for each enterprise. A combination of random forest and extreme gradient boosting are used for indicator screening, and finally, risk scorecard based on logistic regression is used for credit risk scoring. Our results show that technological innovation indicators obtained through text mining provide valuable information for credit risk assessment, and that the combination of ensemble learning from random forest and extreme gradient boosting combinations with logistic regression models outperforms other traditional methods. The best results achieved 0.9129 area under receiver operating characteristic. In addition, our approach provides meaningful scoring rules for credit risk assessment of technology innovation enterprises

Population dynamical behavior of Lotka-Volterra system under regime switching

In this paper, we investigate a Lotka-Volterra system under regime switching dx(t) = diag(x1(t); : : : ; xn(t))[(b(r(t)) + A(r(t))x(t))dt + (r(t))dB(t)]; where B(t) is a standard Brownian motion. The aim here is to find out what happens under regime switching. We first obtain the sufficient conditions for the existence of global positive solutions, stochastic permanence and extinction. We find out that both stochastic permanence and extinction have close relationships with the stationary probability distribution of the Markov chain. The limit of the average in time of the sample path of the solution is then estimated by two constants related to the stationary distribution and the coefficients. Finally, the main results are illustrated by several examples

Stationary distribution of stochastic SIRS epidemic model with standard incidence

We study stochastic versions of a deterministic SIRS (Susceptible, Infective, Recovered, Susceptible) epidemic model with standard incidence. We study the existence of a stationary distribution of stochastic system by the theory of integral Markov semigroup. We prove the distribution densities of the solutions can converge to an invariant density in L1. This shows the system is ergodic. The presented results are demonstrated by numerical simulations

A Novel Manganese Efflux System, YebN, Is Required for Virulence by Xanthomonas oryzae pv. oryzae

Manganese ions (Mn2+) play a crucial role in virulence and protection against oxidative stress in bacterial pathogens. Such pathogens appear to have evolved complex mechanisms for regulating Mn2+ uptake and efflux. Despite numerous studies on Mn2+ uptake, however, only one efflux system has been identified to date. Here, we report on a novel Mn2+ export system, YebN, in Xanthomonas oryzae pv. oryzae (Xoo), the causative agent of bacterial leaf blight. Compared with wild-type PXO99, the yebN mutant was highly sensitive to Mn2+ and accumulated high concentrations of intracellular manganese. In addition, we found that expression of yebN was positively regulated by Mn2+ and the Mn2+-dependent transcription regulator, MntR. Interestingly, the yebN mutant was more tolerant to methyl viologen and H2O2 in low Mn2+ medium than PXO99, but more sensitive in high Mn2+ medium, implying that YebN plays an important role in Mn2+ homoeostasis and detoxification of reactive oxygen species (ROS). Notably, deletion of yebN rendered Xoo sensitive to hypo-osmotic shock, suggesting that YebN may protect against such stress. That mutation of yebN substantially reduced the Xoo growth rate and lesion formation in rice implies that YebN could be involved in Xoo fitness in host. Although YebN has two DUF204 domains, it lacks homology to any known metal transporter. Hence, this is the first report of a novel metal export system that plays essential roles in hypo-osmotic and oxidative stress, and virulence. Our results lay the foundations for elucidating the complex and fascinating relationship between metal homeostasis and host-pathogen interactions

The threshold of a stochastic SIRS epidemic model in a population with varying size

In this paper, a stochastic susceptible-infected-removed-susceptible (SIRS) epidemic model in a population with varying size is discussed. A new threshold ~R0 is identified which determines the outcome of the disease. When the noise is small, if ~R0 1, the infected proportion persists in the mean and we derive that the disease is endemic. Furthermore, when R0 > 1 and subject to a condition on some of the model parameters, we show that the solution of the stochastic model oscillates around the endemic equilibrium of the corresponding deterministic system with threshold R0, and the intensity of fluctuation is proportional to that of the white noise. On the other hand, when the noise is large, we find that a large noise intensity has the effect of suppressing the epidemic, so that it dies out. These results are illustrated by computer simulations

A Text Mining and Ensemble Learning Based Approach for Credit Risk Prediction

Traditional credit risk prediction models mainly rely on financial data. However, technological innovation is the main driving force for the development of enterprises in strategic emerging industries, which is closely related to enterprise credit risk. In this paper, a novel prediction framework utilizing technological innovation text mining data and ensemble learning is proposed. The empirical data from China listed enterprises in strategic emerging industries were applied to construct prediction models using the classification and regression tree model, the random forest model and extreme gradient boosting model. The results show that the model uses the technological innovation text mining data proven to have significant predict ability, and top management teamꞌs attention to innovation variables offer the best prediction capacities. This work improves the application value of enterprise credit risk prediction models in strategic emerging industries by embedding the mining of technological innovation text information